Instructions to use pinecoresystems/MiniMax-Music3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pinecoresystems/MiniMax-Music3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pinecoresystems/MiniMax-Music3", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 2,626 Bytes
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license: other
license_name: minimax-music3-community-license
license_link: LICENSE
library_name: diffusers
pipeline_tag: text-to-audio
tags: [music-generation, text-to-music]
---
# MiniMax Music 3 (TinyPine mirror)
**MiniMax-Music3** by [MiniMax](https://www.minimax.io), unmodified, in its diffusers layout, mirrored so
TinyPine Studio's installer does not depend on third-party download links. TinyPine did not train or
change the model. Upstream: [MiniMaxAI/MiniMax-Music3](https://huggingface.co/MiniMaxAI/MiniMax-Music3).
Licence: the MiniMax-Music3 Community License in `LICENSE` (copyright (c) 2026 MiniMax), including its
Acceptable Use Policy (Exhibit A). Commercial products that use the model must show "MiniMax-Music3" in
their interface; above USD 20 million yearly revenue a separate authorisation from MiniMax is required.
Also included: the AudioSeal watermark generator and detector (Meta, MIT, `audioseal/LICENSE`), which
TinyPine embeds in every song it makes, from [facebook/audioseal](https://huggingface.co/facebook/audioseal).
Only the diffusers sub-folders are mirrored; the SGLang layout (`qwen_7B/`, `flowmatching_vae.pth`,
`dav.pth`) is not.
SHA-256 of the large files:
- `condition_encoder/diffusion_pytorch_model.safetensors` 83179c5eaa9a68a370affe0c1b96c2179f659ea4175666b31071490a202c2a4d
- `language_model/model-00001-of-00004.safetensors` 1e95924a158b45b67cfad04fa0f0a352e5a263e69338734d7c1d558be776cc98
- `language_model/model-00002-of-00004.safetensors` fde2d335eef7eafa268d35e53d4b040ee51d2f3fa727f9855b4c97d88a2892da
- `language_model/model-00003-of-00004.safetensors` 6549a831637ab678a573047e7fa8fdf966998c4f45330e6eb7573d67f7b9b4c6
- `language_model/model-00004-of-00004.safetensors` a1c9865f357b64d3ae9f428fb98a844831e5a0aee9ed17b19bd7f7be32b964a9
- `rvq_depth_decoder/diffusion_pytorch_model.safetensors` 80e86ab006bb5c8d4f223205045035a1c77b2bb9c3be9a86d7e740bbafe5ddf0
- `tokenizer/tokenizer.json` b1537fa9e59a537276ecbc2e12d0438edff635a1f4a4948e679774b5feb3e610
- `transformer/diffusion_pytorch_model-00001-of-00002.safetensors` 1943baebe93b5d5a57e9e1f603d9cef51a73a55e7a42f52913932dc06dcf10f0
- `transformer/diffusion_pytorch_model-00002-of-00002.safetensors` b2f9e29d241e107e5a281f8b369d6db48eee983917df966c56d7b8dd0bd482c2
- `vocoder/diffusion_pytorch_model.safetensors` daf0bf0560ea978ce7b5ec3e82f420a44429efef18bdfbe3c714d55ceffbe934
- `audioseal/generator_base.pth` 7a845b5fbe9364a63a3909d8ab3fe064d13a76ae4c2e983573e08c69b7b51748
- `audioseal/detector_base.pth` 8a78e8a83584113523e161fc599fcab10fd0e94c04d2eb9d2fa1e9ec91ab69d9
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